





'''simple docstring'''


import os
import re
import shutil
import sys
import tempfile
import unittest

import black


lowerCAmelCase_ : Dict	  =       os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
lowerCAmelCase_ : List[str]	  =       "    def __init__(self, config):\n        super().__init__()\n        self.transform = BertPredictionHeadTransform(config)\n\n        # The output weights are the same as the input embeddings, but there is\n        # an output-only bias for each token.\n        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n        self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n        self.decoder.bias = self.bias\n\n    def forward(self, hidden_states):\n        hidden_states = self.transform(hidden_states)\n        hidden_states = self.decoder(hidden_states)\n        return hidden_states\n"






class    UpperCamelCase__	(		unittest.TestCase  ):







	def     __a (					self      : Optional[int]		):




				'''simple docstring'''
				a__	 =			tempfile.mkdtemp()
				os.makedirs(os.path.join(self.transformer_dir			,			"models/bert/"		)		)
				a__	 =			self.transformer_dir
				shutil.copy(
				    os.path.join(_lowerCamelCase			,			"src/transformers/models/bert/modeling_bert.py"		)			,			os.path.join(self.transformer_dir			,			"models/bert/modeling_bert.py"		)			,			)







	def     __a (					self      : List[Any]		):




				'''simple docstring'''
				a__	 =			"src/transformers"
				shutil.rmtree(self.transformer_dir		)







	def     __a (					self      : int			,			lowerCamelCase      : List[str]			,			lowerCamelCase      : Any			,			lowerCamelCase      : Dict			,			lowerCamelCase      : List[Any]=None		):




				'''simple docstring'''
				a__	 =			comment + F'''\nclass {class_name}(nn.Module):\n''' + class_code
				if overwrite_result is not None:
							a__	 =			comment + F'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
				a__	 =			black.Mode(target_versions={black.TargetVersion.PYaa}			,			line_length=1_1_9		)
				a__	 =			black.format_str(_lowerCamelCase			,			mode=_lowerCamelCase		)
				a__	 =			os.path.join(self.transformer_dir			,			"new_code.py"		)
				with open(_lowerCamelCase			,			"w"			,			newline="\n"		) as f:
							f.write(_lowerCamelCase		)
				if overwrite_result is None:
							self.assertTrue(len(check_copies.is_copy_consistent(_lowerCamelCase		)		) == 0		)
				else:
							check_copies.is_copy_consistent(f.name			,			overwrite=_lowerCamelCase		)
							with open(_lowerCamelCase			,			"r"		) as f:
										self.assertTrue(f.read()			,			_lowerCamelCase		)







	def     __a (					self      : Tuple		):




				'''simple docstring'''
				a__	 =			check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead"		)
				self.assertEqual(_lowerCamelCase			,			_lowerCamelCase		)







	def     __a (					self      : Optional[Any]		):




				'''simple docstring'''
				# Base copy consistency
				self.check_copy_consistency(
				    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"			,			"BertLMPredictionHead"			,			REFERENCE_CODE + "\n"			,			)

				# With no empty line at the end
				self.check_copy_consistency(
				    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"			,			"BertLMPredictionHead"			,			_lowerCamelCase			,			)

				# Copy consistency with rename
				self.check_copy_consistency(
				    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"			,			"TestModelLMPredictionHead"			,			re.sub("Bert"			,			"TestModel"			,			_lowerCamelCase		)			,			)

				# Copy consistency with a really long name
				a__	 =			"TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
				self.check_copy_consistency(
				    F'''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'''			,			F'''{long_class_name}LMPredictionHead'''			,			re.sub("Bert"			,			_lowerCamelCase			,			_lowerCamelCase		)			,			)

				# Copy consistency with overwrite
				self.check_copy_consistency(
				    "# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"			,			"TestModelLMPredictionHead"			,			_lowerCamelCase			,			overwrite_result=re.sub("Bert"			,			"TestModel"			,			_lowerCamelCase		)			,			)







	def     __a (					self      : List[Any]		):




				'''simple docstring'''
				a__	 =			check_copies.LOCALIZED_READMES["README_zh-hans.md"]

				a__	 =			(
				    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
				    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
				    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
				    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
				    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
				    " released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
				    " lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
				    " method has been applied to compress GPT2 into"
				    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
				    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
				    " Multilingual BERT into"
				    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
				    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
				    " (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
				    " as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
				    " Luong, Quoc V. Le, Christopher D. Manning."
				)
				a__	 =			(
				    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
				    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
				    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
				    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
				)
				a__	 =			(
				    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
				    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
				    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
				    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
				    " **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
				    " [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
				    " lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
				    " method has been applied to compress GPT2 into"
				    " [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
				    " [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
				    " Multilingual BERT into"
				    " [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
				    " version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
				    " Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
				    " than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
				    " Christopher D. Manning 发布。\n"
				)

				a__ ,					a__	 =			check_copies.convert_to_localized_md(
				    _lowerCamelCase			,			_lowerCamelCase			,			localized_readme["format_model_list"]		)

				self.assertFalse(_lowerCamelCase		)
				self.assertEqual(_lowerCamelCase			,			_lowerCamelCase		)

				a__ ,					a__	 =			check_copies.convert_to_localized_md(
				    _lowerCamelCase			,			_lowerCamelCase			,			localized_readme["format_model_list"]		)

				# Check whether the number of models is equal to README.md after conversion.
				self.assertTrue(_lowerCamelCase		)

				a__	 =			(
				    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
				    " Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
				    " Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
				    " Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
				)
				a__	 =			(
				    "1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
				    " the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
				    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
				    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
				)
				a__	 =			(
				    "1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
				    " Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
				    " Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
				    " Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
				)

				a__ ,					a__	 =			check_copies.convert_to_localized_md(
				    _lowerCamelCase			,			_lowerCamelCase			,			localized_readme["format_model_list"]		)

				# Check if the model link is synchronized.
				self.assertEqual(_lowerCamelCase			,			_lowerCamelCase		)




"""simple docstring"""


from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   List[Any]:




						'''simple docstring'''



						def is_in_circle(__lowerCAmelCase  ,       __lowerCAmelCase							) -> bool:
												lowerCamelCase__   =sqrt((x**2) + (y**2)							)
												# Our circle has a radius of 1, so a distance
												# greater than 1 would land outside the circle.
												return distance_from_centre <= 1

						# The proportion of guesses that landed in the circle
						lowerCamelCase__   =mean(
						    int(is_in_circle(uniform(-1.0  ,       1.0							)  ,       uniform(-1.0  ,       1.0							)							)							)
						    for _ in range(__lowerCAmelCase							)							)
						# The ratio of the area for circle to square is pi/4.
						lowerCamelCase__   =proportion * 4
						print(F'''The estimated value of pi is {pi_estimate}'''							)
						print(F'''The numpy value of pi is {pi}'''							)
						print(F'''The total error is {abs(pi - pi_estimate							)}'''							)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = 0.0  ,       __lowerCAmelCase = 1.0  ,       )				->   float:




						'''simple docstring'''



						return mean(
						    function_to_integrate(uniform(__lowerCAmelCase  ,       __lowerCAmelCase							)							) for _ in range(__lowerCAmelCase							)							) * (max_value - min_value)





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase = 0.0  ,       __lowerCAmelCase = 1.0							)				->   None:




						'''simple docstring'''



						def identity_function(__lowerCAmelCase							) -> float:
												return x

						lowerCamelCase__   =area_under_curve_estimator(
						    __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   =(max_value * max_value - min_value * min_value) / 2

						print("******************"							)
						print(F'''Estimating area under y=x where x varies from {min_value} to {max_value}'''							)
						print(F'''Estimated value is {estimated_value}'''							)
						print(F'''Expected value is {expected_value}'''							)
						print(F'''Total error is {abs(estimated_value - expected_value							)}'''							)
						print("******************"							)





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   None:




						'''simple docstring'''



						def function_to_integrate(__lowerCAmelCase							) -> float:
												return sqrt(4.0 - x * x							)

						lowerCamelCase__   =area_under_curve_estimator(
						    __lowerCAmelCase  ,       __lowerCAmelCase  ,       0.0  ,       2.0							)

						print("******************"							)
						print("Estimating pi using area_under_curve_estimator"							)
						print(F'''Estimated value is {estimated_value}'''							)
						print(F'''Expected value is {pi}'''							)
						print(F'''Total error is {abs(estimated_value - pi							)}'''							)
						print("******************"							)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


